Therapy Contamination as a Measure of Therapist Treatment Adherence in a Trial of Cognitive Behaviour Therapy versus Befriending for Psychosis
Bibliographic record
Abstract
BACKGROUND: High quality randomized controlled trials (RCT) of psychotherapeutic interventions should ensure that the therapy being tested is what is actually delivered. However, contamination of one therapy into the other, a critical component of treatment adherence, is seldom measured in psychotherapy trials of psychosis. AIMS: The aim of the study was to determine whether a purpose-designed measure, the ACE Treatment Integrity Measure (ATIM) could detect therapy contaminations within a controlled trial of cognitive behavioural therapy (CBT) versus Befriending for first-episode psychosis and to compare the ATIM to a more traditional adherence measure, the Cognitive Therapy Scale (CTS). METHOD: Therapy sessions were audio-recorded and at least one therapy session from 53 of the 62 participants in the RCT was rated by an independent rater using the CTS and ATIM. RESULTS: Ninety-nine therapy sessions were rated. All Befriending sessions and all but three CBT sessions were correctly identified. The ATIM showed that 29 of the 99 (29%) sessions were contaminated by techniques from the other therapy. Within the CBT sessions, 19 of the 51 sessions (37%) were contaminated by one or more Befriending techniques. Of the Befriending sessions, 10 of 48 (21%) were contaminated by ACE techniques. The mean CTS score was higher in the CBT than the Befriending group. CONCLUSIONS: The ATIM was able to detect contaminations and revealed more meaningful, fine-grained analysis of what therapy techniques were being delivered and what contaminations occurred. The study highlights the benefit of employing purpose-designed measures that include contamination when assessing treatment adherence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.131 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".